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ORCID

Okan Bulut: https://orcid.org/0000-0001-5853-1267

Yi Zheng: https://orcid.org/0000-0003-2671-0820

Abstract

This editorial introduces the second part of CEJEME's Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement. Building on the foundational discussions in Part 1, this installment further explores the evolving role of AI and ML in assessment, evaluation, and learning analytics. The four articles in this issue examine a broad spectrum of topics, including a survey on the use of ML in the measurement community, an investigation into the effectiveness of digital tools in math assessments, an analysis of complex log data using advanced clustering techniques, and a study on mitigating bias in AI-driven assessments. These contributions provide deeper insights into the methodological advancements and ethical considerations necessary for integrating AI and ML into educational measurement. As the field continues to evolve, this special issue underscores the need for open conversations and collaborations among measurement professionals to ensure that ML/AI-powered assessments are not only technologically sophisticated but also equitable, transparent, accountable, and truly supportive of diverse learners. A final installment of this special issue will follow in the coming months.

DOI

https://doi.org/10.59863/PFLW4433

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